In [1]:

    

from IPython.display import HTML

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')


    

    
Out[1]:

In [2]:

    

import json
import pandas as pd
import matplotlib
from pandas import DataFrame as df
import numpy as np
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (10, 8)

%matplotlib inline
import pandas as pd

from IPython.core.display import HTML
css = open('style-table.css').read() + open('style-notebook.css').read()
HTML('<style>{}</style>'.format(css))


    

    




Populating the interactive namespace from numpy and matplotlib






    
Out[2]:

In [ ]:

    




    

In [3]:

    

path = 'RiskaOS.csv'
open(path).readline()
data = pd.read_csv(path)
data.head()


    

    
Out[3]:






  

    

      

      
RRO
      
RRS
      
TampilanO
      
TampilanS
      
MenuO
      
MenuS
      
PackagingO
      
PackagingS
      
WifiO
      
WifiS
      
PembayaranO
      
PembayaranS
      
PelayananO
      
PelayananS
    
  
  

    

      
0
      
3
      
1
      
S
      
S
      
S
      
S
      
S
      
S
      
S
      
S
      
S
      
S
      
S
      
S
    
    

      
1
      
3
      
3
      
S
      
RG
      
S
      
RG
      
S
      
RG
      
RG
      
RG
      
S
      
S
      
S
      
S
    
    

      
2
      
1
      
1
      
SS
      
RG
      
SS
      
S
      
SS
      
S
      
SS
      
TS
      
SS
      
SS
      
SS
      
S
    
    

      
3
      
2
      
2
      
S
      
S
      
S
      
RG
      
RG
      
RG
      
RG
      
RG
      
RG
      
S
      
S
      
S
    
    

      
4
      
1
      
4
      
S
      
STS
      
S
      
STS
      
S
      
STS
      
S
      
TS
      
RG
      
RG
      
SS
      
TS
    
  

In [4]:

    

path = 'RiskaOS.csv'
open(path).readline()
data = pd.read_csv(path)

data = data.replace({'RRO' : { 5 : '1', 4 : '2', 2 : '4', 1 : '5' }})
data = data.replace({'RRS' : { 5 : '1', 4 : '2', 2 : '4', 1 : '5' }})
data = data.replace({'TampilanO' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'TampilanS' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'MenuO' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'MenuS' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'PackagingO' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'PackagingS' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'WifiO' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'WifiS' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'PembayaranO' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'PembayaranS' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'PelayananO' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})
data = data.replace({'PelayananS' : {  'SS' : 5, 'S' : 4, 'RG' : 3, 'TS' : 2, 'STS' : 1}})

data.head()


    

    




/home/qbits7/anaconda2/lib/python2.7/site-packages/pandas/core/internals.py:4288: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  comp = (nn == nn_at)






    
Out[4]:






  

    

      

      
RRO
      
RRS
      
TampilanO
      
TampilanS
      
MenuO
      
MenuS
      
PackagingO
      
PackagingS
      
WifiO
      
WifiS
      
PembayaranO
      
PembayaranS
      
PelayananO
      
PelayananS
    
  
  

    

      
0
      
3
      
5
      
4
      
4
      
4
      
4
      
4
      
4
      
4
      
4
      
4
      
4
      
4
      
4
    
    

      
1
      
3
      
3
      
4
      
3
      
4
      
3
      
4
      
3
      
3
      
3
      
4
      
4
      
4
      
4
    
    

      
2
      
5
      
5
      
5
      
3
      
5
      
4
      
5
      
4
      
5
      
2
      
5
      
5
      
5
      
4
    
    

      
3
      
4
      
4
      
4
      
4
      
4
      
3
      
3
      
3
      
3
      
3
      
3
      
4
      
4
      
4
    
    

      
4
      
5
      
2
      
4
      
1
      
4
      
1
      
4
      
1
      
4
      
2
      
3
      
3
      
5
      
2
    
  

In [5]:

    

import csv
data.to_csv('RiskaBersih.csv')


    

In [6]:

    

data1 = pd.read_csv('RiskaBersih.csv')


    

In [ ]: